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arXiv · 2502.06403

The AI off-switch problem as a signalling game: bounded rationality and incomparability

Alessio Benavoli·Alessandro Facchini·Marco Zaffalon
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Abstract

The off-switch problem is a critical challenge in AI control: if an AI system resists being switched off, it poses a significant risk. In this paper, we model the off-switch problem as a signalling game, where a human decision-maker communicates its preferences about some underlying decision problem to an AI agent, which then selects actions to maximise the human's utility. We assume that the human is a bounded rational agent and explore various bounded rationality mechanisms. Using real machine learning models, we reprove prior results and demonstrate that a necessary condition for an AI system to refrain from disabling its off-switch is its uncertainty about the human's utility. We also analyse how message costs influence optimal strategies and extend the analysis to scenarios involving incomparability.

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Alessio Benavoli, Alessandro Facchini, Marco Zaffalon. 2025-02-10. The AI off-switch problem as a signalling game: bounded rationality and incomparability. https://arxiv.org/abs/2502.06403

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